MarkerBasedDecomposition: Performs marker-based decomposition of bulk expression using...
In BisqueRNA: Decomposition of Bulk Expression with Single-Cell Sequencing

Description Usage Arguments Details Value Examples

Estimates relative abundances of cell types from PCA-based decomposition. Uses a list of marker genes to subset the expression data, and returns the first PC of each sub-matrix as the cell type fraction estimates. Optionally, weights for each marker gene can be used to prioritize genes that are highly expressed in the given cell type.

MarkerBasedDecomposition(
  bulk.eset,
  markers,
  ct_col = "cluster",
  gene_col = "gene",
  min_gene = 5,
  max_gene = 200,
  weighted = FALSE,
  w_col = "avg_logFC",
  unique_markers = TRUE,
  verbose = TRUE
)

`bulk.eset`	Expression Set. Normalized bulk expression data.
`markers`	Data frame with columns specifying cluster and gene, and optionally a column for weights, typically the fold-change of the gene. Important that the genes for each cell type are row-sorted by signficance.
`ct_col`	Character string. Column name specifying cluster/cell type corresponding to each marker gene in markers.
`gene_col`	Character string. Column name specifying gene names in markers.
`min_gene`	Numeric. Min number of genes to use for each cell type.
`max_gene`	Numeric. Max number of genes to use for each cell type.
`weighted`	Boolean. Whether to use weights for gene prioritization
`w_col`	Character string. Column name for weights, such as "avg_logFC", in markers
`unique_markers`	Boolean. If TRUE, subset markers to include only genes that are markers for only one cell type
`verbose`	Boolean. Whether to print log info during decomposition. Errors will be printed regardless.

Note that this method expects the input bulk data to be normalized, unlike the reference-based method.

A List. Slot bulk.props contains estimated relative cell type abundances. Slot var.explained contains variance explained by first 20 PCs for cell type marker genes. Slot genes.used contains vector of genes used for decomposition.

library(Biobase)
sim.data <- SimulateData(n.ind=10, n.genes=100, n.cells=100,
                         cell.types=c("Neurons", "Astrocytes", "Microglia"),
                         avg.props=c(.5, .3, .2))
res <- MarkerBasedDecomposition(sim.data$bulk.eset, sim.data$markers, weighted=FALSE)
estimated.cell.proportions <- res$bulk.props